Artificial neural networks (ANNs) can provide state-of-the-art performance on traditional image classification tasks. Yet, behind this success, there remains the assumption that training inputs are independently and identically distributed (IID). Typically, data is randomly shuffled prior to training, reducing the dependence of consecutive examples. However, this is not always possible; in the real world, samples tend to be temporally correlated, for instance a robot learning new objects classes may first see many images of a dog, then many of a cat. With ANNs, the result of such correlation is a strong interference between learned parameters important to the prediction of current and previously seen classes – causing the model to forget previously learned classes almost entirely. An ideal remedy for such catastrophic forgetting (CF) would involve the storage and interleaved replay of past examples, thus removing any temporal correlation. In practice, however, the storage of an every-growing set of samples is infeasible. Taking inspiration from the brain, a more tractable solution involves the incorporation of a generative model—such as VAE or GAN—into the continual learner, allowing for previously seen classes to be "replayed" alongside the current classes, in much the same way memories are replayed in the mind. The current state-of-the-art generative model, on which this paper builds [1]}, is able to obtain close to storage based performance on a 10-class MNIST dataset, however, when applied to the more challenging 100-class CIFAR100 dataset, classification precision compared to the stored approach is greatly reduced—with earlier seen classes being affected the most. This paper aims to address the reduction in early class performance by imitating the newfound role of feature exaggeration for the reduction of memory interference in the brain.
